However, unlike the former two types, the TAM category of the verb is redundantly coded for by the combination of subject agreement affixes.

analysis of a stem should return at least the root and the derivational category, and generation should start with a root and a derivational category and return a stem.


RHS can be transformed into this same expression with a similar line of reasoning (and using associativity of ).


Despite this apparent separation between the lexical and the syntactic levels, both are usually estimated solely from a single treebank.

while PCFGs can be accurate, they suffer from vocabulary coverage problems: treebanks are small and lexicons induced from them are limited.


We follow the intuition that external lexical resources are needed only when the information contained in the treebank is too sparse.

we use treebank-derived estimates for reliable events, and resort to the external resources only in the cases of rare or OOV words, for which the treebank distribution is not reliable.


The Viterbi parse over the lattice chooses a lattice path, which induces a segmentation over the input sentence.

parsing and segmentation are performed jointly.


One such advantage is its considerable shortterm memory: storing long sequences of words is not a problem for a computer system.

compensatory strategies are not necessary, regardless of the speaking rate of the speaker.


Moreover, his description is sometimes metaphorical and uses a rather colloquial register.

while the interpreter finds it easier to deal with these excerpts (known vocabulary among others) and L043-3 seems to be more complicated (domain-specific, technical aspect), the automatic system finds it more complicated with the former and less with the latter.


In addition, their approach uses part of the dataset to extend the gazetteer.

the resulting gazetteer list is specific to a particular domain; on the other hand, our approach does not generate a domain-specific list, since it makes use of Wikipedia articles.


When processing "Smith," "Smith College" is the longest sequence that starts with "Smith" and appears in the title list (as an ORG).

we label all occurrences of "Smith College" in the text as an ORG.


The high ambiguity rate inherent in our weakly supervised approach substantially complicates the POS tagging process.

our third goal of this paper is to propose two potentially performance-enhancing extensions to G&G's Bayesian POS tagging approach, which exploit morphology and techniques successfully used in supervised POS tagging.


Intuitively, the tag transition distributions and the output distributions in an HMM-based POS tagger are sparse multinomials.

it is logical to choose a Dirichlet prior with α < 1.


This makes Lexicon 2 a bit more realistic than Lexicon 1 in terms of the way they are constructed.

in comparison to Lexicon 1, Lexicon 2 has a considerably higher ambiguous token rate and unseen word rate: its ambiguous token rate ranges from 64.3% with 5.3 tags/token (d=1) to 80.5% with 8.6 tags/token (d=10), and its unseen word rate ranges from 25% (d=1) to 50% (d=10).


However, this combined factors into target words which don't exist naturally and bring down translation quality.

the decoding is constrained by decomposing into two translation models; a model with POS-tag phrase pairs only and one which jointly translates POS-tags and surface forms.


For those BLC not representing enough concepts according to the threshold, the process selects the next local maximum following the hypernymy hierarchy.

depending on the type of relations considered to be counted and the threshold established, different sets of BLC can be easily obtained for each WN version.


In general, the results obtained by BLC20 are not very different to the results of BLC50.

we can select a medium level of abstraction, without having a significant decrease of the perfor- mance.


Following (Esuli and Sebastiani, 2007), we assumed that there is a semantic relationship between a synset and each word used in its gloss.

the run gloss.100 uses a graph with 70,192 synsets, 83,194 words and 350,855 directed arcs from synsets to lemmas of all words in their glosses.


The AUC value corresponds to the probability that a randomly drawn positive instance will be ranked higher than a randomly drawn negative instance.

an AUC of 0.5 corresponds to random performance, a value of 1.0 corresponds to perfect performance.


Since German has a relatively free word order, it is impossible to deduce the grammatical function of a noun phrase from the configuration of the sentence.

an evaluation based solely on syntactic constituent labels would be meaningless (cf.


For reranking, the features of Collins and Koo (2005) were extended in the following way: Since the German treebank used for our experiments includes grammatical function information on almost all levels in the tree, all feature types were also included with grammatical functions attached: All nodes except the root node of the subtree in question were annotated with their grammatical information.

for the noun phrase (NX) rule with grandparent prepositional phrase (PX) PX GP NX → ART ADJX NN, we add an additional rule PX GP NX-HD → ART ADJX NN-HD.


Another solution consists of generating all possible scopes around the coordination.

for the sentence in (6), the conjunction is aber.


In effect, this means a combination of the methods of experiments 1 (n-best) and 3 (automatic scope).

if the results from this experiment are very close to the results from experiment 1 (nbest), we can conclude that adding the parses with automatic scope readings does not add new information.


However, if we remove these chunk boundaries, the number of possible conjuncts increases dramatically, and parsing times become prohibitive.

we will need to find a good balance between these two needs.


Even letters that are considered as representing vowels may sometimes represent consonants, specifically ו [v]/[o]/ [u] and י [y]/ [i].

the mapping between Hebrew orthography and phonology is highly ambiguous.


In contrast to English (and many other languages), proper names in Hebrew are not capitalized.

the following homographs may be interpreted as either a proper name, a noun, or a verb: One usually distinguishes between two types of transliteration (Knight and Graehl, 1997): Forward transliteration, where an originally Hebrew term is to be transliterated to English; and Backward transliteration, in which a foreign term that has already been transliterated into Hebrew is to be recovered.


7, and there is only a finite set of choices of new dominance atoms that satisfy condition 3.


Together, this shows that L(Γ (G)) = L V (G).

: Theorem 1 The sets of valency trees generated by PF-CCG are regular tree languages.


While the number of highly non-projective dependency structures is negligible for practical applications (Kuhlmann and Nivre, 2006), the rank cannot easily be bounded.

we present an algorithm that transforms the extracted grammar into a normal form that has rank 2, and thus can be parsed more efficiently.


The relative frequency of analyses with a block-degree 2 is almost negligible (Havelka, 2007); the bigger obstacle in applying the treebank grammar is the rank of the resulting LCFRS.

in the remainder of the paper, we present an algorithm that can transform the productions of the input grammar G into an equivalent set of productions with rank at most 2, while preserving the fan-out.


It is not difficult to see that, when p is not connected, we can always split it into new productions of lower rank.

throughout this section we assume that LCFRS only have connected productions.


Since v and v 1 are disjoint, I will also be adjacent to some interval in v 1˚v2 .

we have 2 we also have that each interval I 12 2 v 1˚v2 is either an interval in v 2 or else the concatenation of exactly two intervals I 1 2 v 1 and I 2 2 v 2 .


Let v be an m-interval of G with fan-out f v .

the incoming and outgoing edges for v can be detected in time O.f v / by inspecting the 2 f v endpoints of v. we can compute G in time O.jpj/.


Clearly, classifying all the examples as negative will achieve a very high classification accuracy, but will be of no practical use.

we measure the ability of a classifier to iden-  Table 5: Precision (p) and recall (r) of some classifiers on the TEST material.


9 However, because of the small training size, and the rather huge OOV rate of the translation tasks we address, we did not train translation models on word-tokens, but at the character level.

a phrase is indeed a sequence of characters.


The history of a dialogue between a user and a dialogue system plays an important role as to what the user might be expected to say next.

most of the studies mentioned in the previous section make various efforts to capture history by including relevant features directly in their classifiers.


The trend in n-best lists is to have a majority of utterances that belong to one or two labels and only one hypothesis belonging to the 'opt' category and/or a few to the 'pos' category.

the idea behind this feature is to extract such potential outliers which are the desired goal for the re-ranker.


The first system that we look at attempts to extract sentences so that the average sentiment of the summary is as close as possible to the entity level sentiment R, which was previously defined in section 2.1.

in this case L can be simply defined as, the model prefers summaries with average sentiment as close as possible to the average sentiment across all the reviews.


This is done by prohibiting the algorithm from including a given positive or negative sentence in the summary if another more positive/negative sentence is not included.

the summary is forced to consist of only the most positive and most negative sentences, the exact mix being dependent upon the overall star rating.


(van Halteren et al., 2001;Megyesi, 2001;Loftsson, 2006).

the quality of the PoS annotation in a corpus (the gold standard annotation) is crucial.


Despite human post-editing, (large) tagged corpora are almost certain to contain errors, because humans make mistakes.

it is important to apply known methods and/or develop new methods for automatically detecting tagging errors in corpora.


As discussed earlier, the longer variation ngrams are more likely to contain true errors than the shorter ones.

we manually inspected all the variations of length ≥ 5 produced by this method (752 in total), but only "browsed through" the variations of length 4 (like the one above; 2070 variations) and of length 3 (7563 variations).


In a morphologically complex language like Icelandic, feature agreement, for example inside noun phrases or between a preposition and a noun phrase, plays an important role.

of the total number of possible errors existing in an Icelandic corpus, feature agreement errors are likely to be prevalent.


One might have expected more difference in precision by using five taggers vs. a single tagger, but note that the languages used in the two experiments, as well as the tagsets, are totally different.

the comparison in precision may not be viable.


Recall from Section 3.3 that rules were written to search for feature agreement errors in the output of IceParser.

a high ratio of the total errors corrected by the shallow parsing method (80.2%) are indeed due to feature agreement mismatches.


The number of signature terms is highly correlated with performance in singledocument summarization (-0.25) but there is practically no correlation for multi-document summaries.

we can expect that the performance prediction will be more accurate for single-document summarization.


All scores are normalized by the maximum score within the year.

the smallest and largest possible differences are 0 and 1 respectively.


The only assumption of most current statistical models (Brown et al., 1993;Vogel et al., 1996;Deng and Byrne, 2005) is that the aligned sentences in such corpora should be segmented into sequences of tokens that are meant to be words.

for languages where word boundaries are not orthographically marked, tools which segment a sentence into words are required.


It has been shown that F-score has a very weak correlation with SMT translation quality in terms of BLEU score (Zhang et al., 2008).

we chose to extrinsically evaluate the performance of our approach via the Chinese-English translation task, i.e.


Hence, the types of relationships they capture do not fully coincide with entailment inference needs.

the definition suggests a specification for the rules that should be provided by a lexical entailment resource, following an operative rationale: a rule e' ⇒ e should be included in an entailment knowledge resource if it would be needed, as part of a compositional process, to infer the meaning of e from some natural texts.


An inference system applies a rule by inferring the rule's RHS from texts that match its LHS.

the utility of a resource depends on the performance of its rule applications rather than on the proportion of correct rules it contains.


Automatic detection of general relations between short texts is a complex task that cannot be carried out only relying on language models and bag-of-words.

learning methods to exploit syntax and semantics are required.


In contrast, discriminative models such as Support Vector Machines (SVMs) have theoretically been shown to be robust to noise and irrelevant features (Vapnik, 1995).

partially correct linguistic structures may still provide a relevant contribution since only the relevant information would be taken into account.


The text pair kernel operates by comparing the content of questions and the content of answers in a separate fashion.

given two pairs p 1 = q 1 , a 1 and p 2 = q 2 , a 2 , a kernel function is defined as , where τ varies across different kernel functions described hereafter.


By construction, we have |V L | = 317, |V C | = 541, |E lc | = 7022, and |E cc | = 30412.

the order of the matrix A is 541 × 317 and that of the matrix B is 541 × 541.


The consonants are once again found to get clustered into three groups, though not as clearly as in the previous case.

in order to determine the basis of the split, we repeat experiments I and II.


Note that this method of inventory construction leads to proportionate co-occurrence.

the first eigenvector components of B R are highly correlated to the occurrence frequency of the consonants.


This is because, such a distinction is prevalent in many other sounds, some of which are (a) nasals in Tamil (Shanmugam, 1972) and Malayalam (Shanmugam, 1972;Ladefoged and Maddieson, 1996), (b) laterals in Albanian (Ladefoged and Maddieson, 1996), and (c) stops in certain dialectal variations of Swahili (Hayward et al., 1989).

it is sensible to conclude that the two distinct groups L + and L − induced by our algorithm are true representatives of two important linguistic typologies.


it turns out that the methodology presented here essentially facilitates the induction of linguistic typologies.


Moreover, while computational lexicons like WordNet contain semantically explicit information such as, among others, hypernymy and meronymy relations, most thesauri, glossaries, and machine-readable dictionaries are often just electronic transcriptions of their paper counterparts.


Then for each content word w in gloss(s) and for each sense s of w , we add the corresponding edge to the dictionary graph, i.e.

: For instance, consider WordNet as our input dictionary D. of step (ii), given the semantic relation "sport 1 n is a hypernym of racing 1 n ", the edge (racing 1 n , sport 1 n ) is added to E (similarly, an inverse edge is added due to the hyponymy relation holding between sport 1 n and racing 1 n ).


Our call to the CQC algorithm in Table 1 is then CQC-Algorithm(flesh 3 n , fruit n ).

of the first two steps of the algorithm, a set of cycles and quasi-cycles for each sense of fruit n is collected, based on a DFS starting from the respective senses of our target word (we assume δ = 5).


the score which leads to a sense assignment) increases.

users can tune the disambiguation performance based on their specific needs (coverage, precision, etc.).


(1998), who describe the construction of Mind-Net, a lexical knowledge base obtained from the automated extraction of lexico-semantic information from two machine-readable dictionaries.

weighted relation paths are produced to infer the semantic similarity between pairs of words.


Deterministic parsing cannot simply be applied to unification-based grammar parsing, which often fails because of its hard constraints.

it is developed by using default unification, which almost always succeeds in unification by overwriting inconsistent constraints in grammars.


These techniques, however, cannot simply be applied to unification-based grammar parsing because it can fail as a result of its hard constraints in the grammar.

in this study, we propose deterministic parsing for unification-based grammars by using default unification, which almost always succeeds in unification by overwriting inconsistent constraints in the grammars.


When types are involved, however, it is not easy to find unifiable path values in the default feature structure.

we implemented a more simply typed version of Corpestake's default unification.


Indeed, the manual enumeration of all attributes of each target concept, to measure recall, is unfeasible.

the evaluation focuses on the assessment of attribute accuracy.


In the rightmost graphs, only target concepts for which some attributes were extracted are included in the precision scores of a given run.

the leftmost graphs properly penalize a run Several conclusions can be drawn after inspecting the results.


The number of target concepts for which some attributes are extracted is 12 for run N, 23 for Y, 17 for E s and 23 for E a .

both run N and run E s exhibit rather binary behavior across individual classes, in that they tend to either not retrieve any attributes or retrieve attributes of relatively higher quality than the other runs, causing E s and N to have the worst precision scores in the last but one row of Table 5, but the best precision scores in the last row of Table 5.


According to Aitchinson (2003), the four major types of associations are, in order of frequency, co-ordination (co-hyponyms like pepper and salt), collocation (like salt and water), superordination (insect as a hypernym of butterfly) and synonymy (like starved and hungry).

a computational model that is able to predict associations accurately has to deal with a wide range of semantic relations.


Probably the most straightforward way to predict strong associations is to assume that a cue and its strong association often co-occur in text.

we can use collocation measures like point-wise mutual information (Church and Hanks, 1989) or the log-likelihood ratio (Dunning, 1993) to predict the strong association for a given cue.


As we have argued before, one characteristic of cues and their strong associations is that they can sometimes be combined into a compound.

we developed a third approach which discovers for every cue the words in the corpus that in combination with it lead to an existing compound.


Associations absent from the list auto-matically received a rank of 101.

the lower the rank, the better the performance of the system.


This relation between the examples can be easily encoded as a graph.

every node in the graph is an example and the edge represents the relation.


The label propagation algorithm minimizes a quadratic energy function where y i and y j are the labels assigned to the nodes i and j respectively.

6 to derive the labels at y i , we set ∂ ∂y i E = 0 to obtain the following update equation In practice, we use the following iterative algorithm as noted by Zhu and Ghahramani (2002).


Therefore realisations of a very frequent type are expected to be realised similar to each other.

similarity and variability are expressions of the same characteristic: the higher the degree of similarity of pitch accent tokens, the lower their realisation variability.


This led to a reduced number of pitch accent tokens: 1021 L * H accents, 571 H * L accents and 134 H * accents.

there is a continuum of frequency of occurrence, high to low, from L * H to H * .


For tokens of the pitch accent types L * H, H * L and H * , each token was modelled using the full set of PaIntE parameters.

each token was represented in terms of a 6-dimensional vector.


These three distributions were tested against each other for significance using the Kolmogorov-Smirnov test (α = 0.017), yielding p-values of p 0.001.

there are significant differences between these distributions.


Unfortunately, this problem is generally domainspecific because indexing terms that are useful in one domain can be ineffective in others.

we present a supervised machine learning approach to image annotation utilizing non-lexical features 1 extracted from image-related text to select useful terms.


We hypothesized that the training data labels may have been highly sensitive to the subjectivity of the reviewers.

we retrained the learner with only those observations made by reviewers A and B (of the five total reviewers) and again compared the classification results with the gold standard.


Although this is relatively low for a classification task, these results improve upon the baseline classification scheme (all extracted terms are useful for indexing) with an F 1 -score of 0.182 .

non-lexical features can be leveraged, albeit to a small degree with our current features and classifier, in automatically selecting useful image indexing terms.


As more audio frames are consumed by the recogniser, the word "four" is no longer the best candidate for this stretch of audio.

the module must now revoke the IU holding the word "four" (marked with a dotted outline) and add a new IU for the word "forty".


Building a fully incremental system with a behaviour more closely resembling that of human dialogue participants raises a series of new challenges.

in order to make the task more feasible, we have chosen a very limited domainwhat might be called a micro-domain (cf.


Obviously, such a dialogue will result in many more speaker-shifts, which in turn increases the need for the rapid turn-taking.

while the number dictation task might appear simple at a first glance (very limited vocabulary, simple syntax and semantics), there are several issues -not typically considered in most dialogue systemswhich an incremental and more human-like dialogue system must address.


Table 2: Accuracy, literal precision (P l ), recall (R l ), and F-Score (F l ) for the classifiers cause we are interested in detecting literal usages among the canonical forms.

we also computed the precision (P l ), recall (R l ), and Fscore (F l ) for the literal class.


for repeating the results and significance testing.

we compare our results only to the tools listed above.


On Czech, using the best feature set, the difference on the development data set is 95.96 % vs. 95.02 %.

all the results presented in the following text use averaging.


Table 5 demonstrates that there is practically no difference in the results.

we use the sequential chopping mechanism, mainly for its simplicity.


However, when we compared this alternative method with the aforementioned approximation on search steps, we found that it worked worse than the latter, in terms of performance and speed.

we focus on the approximation on search steps in this paper.


The cross-language document similarity model requires a bilingual dictionary in the appropriate language pair.

we generated statistical dictionaries for 3 language pairs (from parallel corpora of the following sizes: 11K sentence pairs in English-Kannada, 54K in English-Hindi, and 14K in English-Tamil) using the GIZA++ statistical alignment tool (Och et al., 2003), with 5 iterations each of IBM Model 1 and HMM.


Such templates match statements with identical structure except at the template's slots.

the factoid A POLITICIAN MAY GIVE A SPEECH would match the second template.


We say senses plural, as the majority of KNEXT propositions select senses that are more coarse-grained than WordNet synsets.

we wish to evaluate these more coarse-grained sense disambiguation results entailed by our type abstractions.


After the -much longer -fourth sentence, recall drops considerably, but precision remains the same.

the f-score is quite a bit higher than 40%: it is over 53%.


For the experiments described in the paper, this constituted an effort of two and a half person-months.

they use only 15.000 training examples (taken from ATIS, so presumably relatively short sentences).


eat prefers edible arguments to fill its patient slot).

what these models capture is actually not semantic role plausibility, but argument plausibility.


However, nouns can also be more or less equally good as agents and patients for a given verb-one is hopefully as likely to be helped by a friend as to help a friend oneselfor equally bad-houses only kill in horror movies, and 'to kill a house' can only be made sense of in a metaphorical way.

we also implement a model that takes distance into account for its plausibility ratings.


To find out whether a nonnormalized model is indeed a better predictor of thematic fit than a normalized model, we also run experiments with a normalized version of the model presented in Section 3.2: Someone familiar with the literature on human categorization behavior might recognize Equation 7; this model is actually simply a Generalized Context Model (GCM) (Nosofsky, 1986), with the 'context' being restricted to the k nearest neighbors of the target item.

we will refer to this model using the shorthand kGCM.


In contrast to previous models, this model does not implement semantic role plausibility as 'fit with verb selectional preferences', but directly captures the semantic role ambiguity problem comprehenders have to solve when confronted with sentences that contain structural ambiguities like the MV/RR ambiguity, namely deciding which semantic role a noun has in the event denoted by the verb.

the model should be easily extensible towards a complete model of any sentence-level ambiguity that revolves around a semantic role ambiguity.


A fine-grained taxonomy can be viewed as a set of triples Tijk = <Ci, Dj, Pk>, where Ci denotes a child of the parent term Pk that possesses the discriminating property Dj; in effect, each such triple expresses that Ci is a specialization of the complex taxonym Dj-Pk.

the belief that cola is a carbonated-drink is expressed by the triple <cola, carbonated, drink>.


We can now denote the knowledge that is acquired when starting from a given seed collection S after t cycles of bootstrapping as Kt S .

web queries, and the small snippets of text that they return, offer just a keyhole view of language as it is used in real documents.


It is noted that the key feature used by Tao and Zhai (2005) is the , score which depends purely on statistical information.

our motivation is to propose more features to link the source and target documents more effectively for a better performance.


The use of words in a news title is typically concise and conveys the essence of the information in the document.

a high score would indicate a high likelihood of similarity between two bilingual documents.


Thus, a high score would indicate a high likelihood of similarity between two bilingual documents.

we use as a quantitative feature in our feature set.


Our feature set is selected based on the need for a method to be adaptable to new language-pairs without relying heavily on linguistic resources, unsupervised learning strategy.

in the proposed method we make use of simple bilingual dictionaries, which are rather inexpensive and easily obtained nowadays.


This makes the search for an optimal tree an NP-hard problem (McDonald and Satta, 2007) as all possible trees must be considered to find an optimal solution.

we must choose a heuristic search algorithm for finding the locally optimum spanning tree.


In , we showed that introducing a model of argument positions into a greedy spanning tree algorithm had little effect on performance.

to consider both directions of competition, we design a new algorithm for constructing (dependency) spanning trees that casts edge selection as a weighted bipartite graph matching (or assignment) problem.


In this paper, we search across all possible tree structures whilst searching for the best word ordering.

an argument model is needed to identify linguistically plausible spanning trees.


In the case presented here, this frequency is 1 (the nearest neighbour).

whereas the assumption with co-occurrence is that the linguistically pertinent words are those that fall within a fixed-sized window of the word of interest, the assumption underpinning co-dispersion is that the relevant information lies (if at all) with the closest neighbouring occurrence of each word type.


The closer to the origin these lines, the fewer word instances were required to be present in the random corpus before high levels of apparent association became unlikely, and so the fewer would be required in a real corpus before we could be confident of the import of a measured level of association.

if word pairs in a real corpus exceed these levels, we say that they show significant association.


However, with Markov logic, it is easy to add predicates and formulas to allow joint inference.

we believe that Markov logic offers more potential to incorporate arbitrary prior knowledge and leverage further opportunities in joint inference.


(15) would be incorrect and a direct sum n C (s, f ) + n C (s, f ) would provide the true count.

we have where the first summation is over all phrase vertices s such that f (s ) = f .


Though different verbs admit different alternations, some alternations are shared across multiple verbs and are very frequent (e.g., passivization, example sentences (a) vs. (d), or dativization: John gave a book to Mary vs. John gave Mary a book) (Levin, 1993).

it is natural to assume that the clusterings should be similar, though not identical, across verbs.


Intuitively, if data for several predicates strongly suggests that two argument keys should be clustered (e.g., there is a large overlap between argument fillers for the two keys) then the posterior will indicate that d i,j is expected to be greater for the pair {i, j} than for some other pair {i , j } for which the evidence is less clear.

argument keys i and j will be clustered even for predicates without strong evidence for such a clustering, whereas i and j will not.


In the coupled model, clusterings for different predicates are statistically dependent, as the similarity structure D is latent and shared across predicates.

a more complex inference procedure is needed.


An SVM with a polynomial kernel takes into account not only individual input features but also their interactions (Manning et al., 2008, chapter 15).

our classifier can capture not just properties of individual dimensions of the antecedent and consequent pairs, but also properties of their combinations (e.g., the product of the first dimensions of the antecedent and the consequent).


In the first (SVM pair-out ), we hold out one quantifier pair as testing data and use the other 29 pairs in Table 1 as training data.

for example, the classifier must discover all dogs |= some dogs without seeing any all N |= some N instance in the training data.


While such sentences do support exactly the same inferences, we are also interested in the inferences that can be made from similar sentences that are not paraphrases according to this strict definition -a situation that is more often encountered in end applications.

we adopt a less restricted notion of paraphrasis.


We use only those cases that have thus been determined to be correct questionanswer pairs.

of this restriction, this task is rather more like Task 1 in how it tests a model's ability to recognize lexical and syntactic paraphrases.


Because our models were trained on newspaper text, they required adaptation to this specialized domain.

we also trained the M&L, E&P and TFP models on the GENIA corpus, back-ing off to word vectors from the GENIA corpus when a word vector could not be found in the Gigaword-trained model.


Typically, every parser is evaluated with respect to its own formal representation type and the underlying theory which it was trained to recover.

numerical scores of parses across experiments are incomparable.


new (or emphatic) information (the focus) always precedes the verb and old information (the topic) precedes the focus position.

the position relative to the verb has no predictive force as regards the syntactic function of the given argument: while in English, the noun phrase before the verb is most typically the subject, in Hungarian, it is the focus of the sentence, which itself can be the subject, object or any other argument (É.


For instance, the preposition by may introduce an adverbial modifier of manner (MNR) in by creating a bill and the agent in a passive sentence (LGS).

labeling adverbs seems to be a more difficult task in English.


If the parser chooses an erroneous transition that leads it to build a dependency link from 1 to 2 instead of the correct link from 2 to 1, this will lead it to a state where the single-head constraint makes it illegal to create the link from 3 to 2.

a single erroneous choice will cause two attachment errors in the output tree.


In our training data we for example find 102 questions matching the pattern When[1]+was[2]+NP[3]+VERB[4], which together list 382 answer sentences, and thus 382 potentially different answer sentence structures from which patterns can be gained.

the amount of training examples we have available, is sufficient to achieve the performance described in Section 7.


We would like to explicitly call out one property of our algorithm: It effectively returns two entities: a) a sentence that constitutes a valid response to the query, b) the head node of a phrase in that sentence that constitutes the answer.

the algorithm can be used for sentence retrieval or for answer retrieval.


The difference between both tables is that the first uses all 1658 questions in our test sets for the evaluation, whereas the second considers only those 1122 questions for which our system was able to learn a pattern.

for Table 10 questions which the system had no chance of answering due to limited training data are omitted.


Since clicks are linked with entire snippets, it is uncertain which sentences are genuine descriptions (see the previous example).

we removed those templates already contained in the negative set, along with those samples that matched an array of well-known handcrafted rules.


Generally, genre can be seen as a sub-problem of domain.

we consider genre to be the general style of the text e.g.


BLEU score for SMT), which is not necessarily correlated with the retrieval quality (especially for the short queries).

for example, the word order which is crucial for translation quality (and is taken into account by most MT evaluation metrics) is often ignored by IR models.


Finally, the reranking framework allows a lot of flexibility.

it allows enriching the baseline translation model with new complex features which might be difficult to introduce into the translation model directly.


Lexicalised features take into account the quality of lexical alignment, by weighting each rectangle (s 1 , s 2 , t 1 , t 2 ) by a probability of aligning s 1 to t 1 and s The Label-Specific features take into account the nature of the aligned dependencies.

the rectangles of the form ((s1, subj, s2), (t1, subj, t2)) will get more weight than a rectangle ((s1, subj, s2), (t1, nmod, t2)).


We didn't have access to all relevance judgements of the previously desribed tracks.

we used only a subset of the previously extracted parallel set, which includes CLEF 2000-2008 topics from the AdHoc-main, AdHoc-TEL and GeoCLEF tracks.


Secondly, we compare different syntax-based features combinations, as described in section 3.2.2.

we compare the following reranking models (defined by the feature set): moses, lex (lexical coupling + moses features), lab (label-specific coupling + moses features), posmaplex (lexical PoS mapping + moses features ), lab-lex (label-specific coupling + lexical coupling + moses features), lablex-posmap (label-specific coupling + lexical coupling features + generic PoS mapping).


We treat each linked article pair as a topic and collect counts for each phrase across all articles in its corresponding language.

the size of a phrase topic signature is the number of article pairs with interlingual links in Wikipedia, and each component contains the number of times the phrase appears in (the appropriate side of) the corresponding pair.


punctuation) are more likely to appear in multiple orientations with a given phrase, and therefore provide poor evidence of reordering.

we (a) collect the longest contextual phrases (which also appear in the phrase table) for reordering feature estimation, and (b) prune the set of sentences so that we only keep a small set of least frequent contextual phrases (this has the effect of dropping many function words and punctuation marks and and relying more heavily on multi-word content phrases to estimate the reordering).


MLE techniques aims at minimising the Kullback-Leibler divergence between the actual unknown probability distribution and the probability model (Bishop, 2006), defined as Pr(e, f ) log Pr(e, f ) p θ (e, f ) (2) When minimising, Eq.

(2) is simplified tô Pr(e, f ) log(p θ (e, f )) (3) which is approximated by a sufficiently large dataset under the commonly hold assumption that it is independently and identically distributed according to Pr(e, f ) aŝ by perturbing the sample {(e n , f n )} with an active strategy, we are, in fact, modifying the approximation to Eq.


This model being conditioned on several pieces of information, it can be affected by data sparsity problems.

the model is actually approximated as an interpolation of probabilities: where λ i , i = 1, ..., 4, are parameters of the model to be tuned, and c h is the cluster of head words for a given entity tag t. With such model, when not all pieces of information are available to estimate reliably the probability with more conditioning, the model can still provide a probability with terms conditioned with less information.


An integration of the same kind of features has been tried also in the model used in this work, without giving significant improvements, but making model training much harder.

this direction has not been further investigated.


The task of linking events and relations to time stamps can be defined as the following: given a set of expressions denoting events or relation men-tions in a document, and a set of time expressions in the same document, find all instances of the t link relation between elements of the two input sets.

the existence of a t link (e, t) means that e, which is an event or a relation mention, occurs within the temporal context specified by the time expression t. our task can be cast as a binary relation classification task: for each possible pair of (event/relation, time) in a document, decide whether there exists a link between the two, and if so, express it in the data.


This is a bit different from our settings as it required the implementation of a multi-class classifier.

we trained three different binary classifiers (using the same feature set) for the first three of those types (for which there was sufficient training data) and we used a one-versus-all strategy to distinguish positive from negative examples.


This annotation procedure roughly requires 3 passes 3 over the same corpus.


The intuition is similar to what we observed from figure 3 for false negatives since we also observed very similar distribution using the model trained with noisy data.

we can purify negatives by removing examples in this noisy subset.


For example, still slightly more than half of the top 2000 examples are true negatives.

we cannot simply flip their labels and use them as positive examples.


Moreover, since, in our method each seed topic is a distribution over the seed words, the convex combination of regular and seed topics can be seen as adding different weights (c i ) to different components of the prior vector.

our Model 1 can be seen as an asymmetric generalization of the Informed priors.


Update summarization is related to novelty detection which can be defined as the problem of determining whether a document contains new information given an existing collection (Soboroff and Harman, 2005).

while the goal of novelty detection is to determine whether some information is new, the goal of update summarization is to extract and synthesize the novel information.


The interested reader is invited to consult (Wang, 2011) for more details on using Gibbs sampling for LDA-like models where k ∈ K, n k denotes the number of times word v is assigned to topic k, and n (cd) k denotes the number of words in document d of collection c that are assigned to topic k. By the strong law of large numbers, the average of sample parameters should converge towards the true expected value of the model parameter.

good estimates of the model parameters can be obtained averaging over the sampled values.


The good news is that this background distribution will contain closed-class words in the language, which are domain-independent (see supplementary material for examples).

we can generate this distribution from one of the TAC datasets only once, and then it can be reused.


In the algorithm, c i represents the cost of the sentence (i.e., length).

the algorithm actually selects sentences with large marginal unity relative to their length (trade-off controlled by the parameter r).


Overall, the pairwise model appears to perform slightly better than the coverage model with the datasets and features we used.

we focus on the pairwise model in the following.


Table 5 displays the figures for the MIX1 corpus: Phrase tables trained on mixed corpora have higher covering set average length, similar covering set entropy, but significantly worse covering set cross-entropy.

improving covering set cross-entropy has the greatest potential for improving translation quality.


Sentences that are determined to have "role-specific" contexts are passed along to the noun phrase classifiers for role filler extraction.

event narrative documents pass through both the lower pathway and the upper pathway.


Noun phrase #3 ("the unidentified men") does not occur in a pattern context, but it is deemed to be coreferent with "two armed men" because they have the same head noun.

we propagate the perpetrator label from noun phrase #1 to noun phrase #3.


We approximate the relevant information density heuristic by computing the ratio of relevant sentences (both event sentences and role-specific sentences) out of all the sentences in a document.

the event narrative labeller only relies on the output of the two sentence classifiers.


As this process continues to iterate over, the system acquires more extraction rules, fanning out from the seed set until no new rules can be learned.

defined, bootstrapping has been used in natural language processing research, notably in word sense disambiguation (Yarowsky, 1995).


For triggers with lower accuracy, this high probability is only achieved when the two mentions share at least 60% of their roles, in addition to having a common trigger.

our approach uses co-occurrence of both trigger and event argument for detecting new event mentions.


However, one document may contain multiple types of events, thus we set a more restricted validation measure on new rules: • Good Match If a new rule "rediscovers" already extracted events of the same type, then it will be counted as either a Full Match or Partial Match based on previous rules • Possible Match If an already extracted event of same type of a rule contains same entities and trigger as the candidate extracted by the rule.

this candidate is a possible match, so it will get a partial  • False Alarm If a new rule picks up an already extracted event in different type event patterns are validated for overall expected precision by calculating the ratio of positive matches to all matches against known events.


For example, the pattern in Figure 8 has an initially low projected accuracy score; however, we find that positive matches of this pattern show a very high (100% in fact) degree of correlation with mentions of Demonstrate events.

limiting the application of this pattern to situations where a Justice-Arrest-Jail event is mentioned in a nearby text improves its projected accuracy to 91%, which is well above the required threshold.


Our task required direct natural language input from workers and did not include any multiple choice questions (which tend to attract more cheating).

we limited our quality control efforts to (1) checking for empty input fields and (2) blocking copy/paste functionality on a form.


We found that 9 out of 12 positive emotion terms (except self-pride, levity and fearlessness) and 9 out of 10 negative emotion terms (except ingratitude) also appear in CLEX as shown in Table  5.

we can conclude that annotators do not associate any colors with self-pride, levity, fearlessness and ingratitude.


Instead, we use the overlapping 289 affect terms between WORDNET-AFFECT and CLEX and propagate labels from WORDNET-AFFECT to the corresponding affect terms in CLEX.

we discover positive and negative affect term associations with the 11 B&K colors.


Recall that annotations in EMOLEX are done by workers from the USA only.

we report two numbers for CLEX -annotations from workers from the USA (C A ) and all annotations (C).


This inconsistency may be explained by several reasons: (a) EMOLEX associates emotions with colors through concepts, but CLEX has color-emotion associations obtained directly from annotators; (b) CLEX has 3,397 affect terms compared to 8 basic emotions in EMOLEX.

it may be introducing some ambiguous color-emotion associations.


Automatic evaluation of coherence is an essential component of various natural language applications.

the study of coherence models has recently become an active research area.


However, as argued by Elsner and Charniak (2011), to better simulate the real situations that human readers might encounter in machine-generated documents, such oracular information should not be taken into account.

we also employ two alternative approaches for entity extraction: (1) use the same automatic coreference resolution tool on permuted documents -we call it the Corefer-ence± condition; (2) use no coreference resolution, i.e., group head noun clusters by simple string matching -B&L's Coreference− condition.


In such a setting, the performance of a model trained from permutations generated from a uniform distribution may suffer some accuracy loss.

in addition to the set of permutations used by Barzilay and Lapata (2008) (PS BL ), we create another set of permutations for each source document (PS M ) by assigning most of the probability mass to permutations which are mostly similar to the original source document.


In addition to these two (sub)domains, we chose some text type that is not even news text in order to have a very distant domain.

we had to use some text not included in the MPQA corpus.


the number of prototypical opinion holders co-occurring with those adjectives was too small.

we widen the semantic type of this position from prototypical opinion holders to persons.


For the opinion holder me in (10), for example, it would correspond to A1 shock.

we introduce for each generalization method an additional feature replacing the sparse lexical item by a generalization label, i.e.


FICTION contains more out-ofdomain language.

rB (which exclusively uses domain-independent knowledge) outperforms both learning-based methods including the ones incorporating generalization.


The input of this model will be a partial script c of n events, where , and the output of this model will be a ranked list of events where the highest ranked events are the ones most likely to belong to the event sequence in the script.

the key issue for this model is to define the function f for ranking events.


The difficulty of a cloze task depends a lot on the context around the missing item -in some cases it may be quite predictable, but in many cases there is no single correct answer, though some answers are more probable than others.

performing well on a cloze task is more about ranking the missing event highly, and not about proposing a single "correct" event.


However, where perplexity measures how good the model is at predicting a script event given the previous events in the script, narrative cloze measures how good the model is at predicting what is missing between events in the script.

narrative cloze is somewhat more appropriate to our task, and at the same time simplifies comparisons to prior work.


Similarly Powers (2003) shows that his Kappa (Informedness) has this property.

it is appropriate to consider the dichotomous case, and from this we can generalize as required.


In fact, when we are using bootstrapping techniques or dealing with multiple real samples or different subjects or ecosystems, Prevalence may also vary.

the simple marginal assumptions of Cohen or Powers statistics are the appropriate ones.


The solution should preferably be language-and project-independent.

we refrain from deeper syntactic parsing, Wikipedia-specific features, and language resources that are limited to English.


In addition, what makes online forums different from other media is that users of online communities are not only the information consumers but also active providers as participants.

in this study we develop a recommendation system to account for these characteristics of forums.


However, the dobj of its VB (ate) is not the second NN (meat), but the subsequent NN (pie).

pOS N-grams predict word relations only in a probabilistic sense.


Note that PONG estimates relative rather than absolute probabilities.

it cannot (and does not) compare them against a fixed threshold to make decisions about selectional preferences.


A semantic concept is represented as a synset, i.e., as a set of words whose individual members (referred to as lexical units) are taken to be (near) synonyms.

a synset is a set-representation of the semantic relation of synonymy.


WebCAGe is developed primarily for the purpose of the word sense disambiguation task.

only those target words that are genuinely ambiguous are included in this resource.


Values in the vector corresponded to the smoothed frequencies with which each word directly preceded or followed the represented word.

each word w was assigned a vector (f w,1 , ..., f w,15508 ), such that f w,v is the number of times word v directly precedes (for v ≤ 7754) or follows (for v > 7754) word w. Next, the frequency counts were transformed into Pointwise Mutual Information (PMI) values (see Equation 2), following Bullinaria and Levy's (2007) findings that PMI produced more psychologically accurate predictions than other measures: Finally, the 400 columns with the highest variance were selected from the 7754×15508-matrix of row vectors, making them more computationally manageable, but not significantly less informative.


That is, considering only the current word's surprisal, as in Frank and Bod's analysis, did not reveal a significant contribution of PSGs over and above RNNs.

additional effects of PSG surprisal might only be apparent when spill-over effects are investigated by taking previous word surprisal as a predictor of RT.


For example, a noun sequence of the form "n 0 n 1 and n 2 n 3 " has as many as five possible structures (Resnik, 1999).

a grammatical approach is not sufficient to disambiguate coordination structures.


We now define the index set for the coordination structure analysis as Each parse tree is a vector y = {y r : r ∈ I csa }, with y r = 1 if rule r is in the parse tree, and y r = 0 otherwise.

each parse tree is represented as a vector in {0, 1} m , where m = |I csa |.


For the coordination structure analysis with alignment-based features we define the extended index set to be I ′ csa = I csa ∪ I uni where Here each triple (a, b, c) represents that word w c is recognized as the last word of the right conjunct and the scope of the left conjunct or the coordinating conjunction is w a ...w b 1 .

each parse-tree vector y will have additional components y a,b,c .


Hybrid LM also differs from POS-level LM in that it uses a word-to-class mapping to determine POS tags.

it doesn't require the decoding overload of factored models nor the tagging of all parallel data used to build phrase tables.


Compiling such lexicons manually is often an expensive and time-consuming task, whereas the methods for mining the lexicons from parallel corpora are not applicable for language pairs and domains where such corpora is unavailable or missing.

the focus of researchers turned to comparable corpora, which consist of documents with partially overlapping content, usually available in abundance.


Therefore the focus of researchers turned to comparable corpora, which consist of documents with partially overlapping content, usually available in abundance.

it is much easier to build a high-volume comparable corpus.


In other words, there can be a production which can be binarized with a parsing complexity that is minimal while its fan-out is sub-optimal.

we focus on parsing complexity rather than fan-out in this work, since parsing complexity determines the actual time complexity of parsing with a grammar.


With discontinuous treebank parsing the asymptotic complexity of the grammar also plays a major role.

we suggest to parse not just with a coarser grammar, but with a coarser grammar formalism, following a suggestion in van Cranenburgh et al.


However, representation details are highly system specific, and difficult and time-consuming to annotate.

we implemented a simplified annotation scheme which classifies whole answers as correct, partially correct but incomplete, or contradictory, without explicitly identifying the correct and incorrect parts.


These sessions were completed prior to the beginning of the experiment during which our evaluation corpus was collected, and are not included in the corpus.

the corpus constitutes unseen testing data for the BEETLE II interpreter.


For example, we only observed 25 instances where a contradictory utterance was miscategorized as correct (compared to 200 "contradictory-pc incomplete" confusions), and so for many students this misclassification was never observed, and predictions based on this feature are not likely to be reliable.

we limited our features to those misclassifications that occurred at least twice for each student (i.e., at least 70 times in the entire corpus).


Previous research shows that students who give more correct or partially correct answers, either in human-human or humancomputer dialogue, exhibit higher learning gains, and this has been established for different systems and tutoring domains (Litman et al., 2009).

% of contradictory answers is negatively predictive of learning gain.


Note, however, that this is over-optimistic: the lexicon contains a tag-class for each word type in our testing dataset, i.e., while there can be word types not seen in the training dataset, there are no word types that are not listed in the lexicon.

this high accuracy is probably due to a large extent to the scale and quality of our morphological lexicon, and it might not be as strong with smaller lexicons; we plan to investigate this in future work.


These features are generated from a set of Web queries.

the collected values are likely to be affected by different noise than that present in the original dataset.


and 'Musical Compositions' is 2 (via 'Symphonies').

the MRR score would be 0.25 (given by the fourth element of the ranked list), whereas the DRR score would be 0.33 (given by the first element of the ranked list).


Concretely, the gold standard is split into five folds such that the sets of annotation labels in each fold are disjoint.

none of the annotation labels in testing appears in training.


After argument disambiguation (e.g., (Dredze et al., 2010)), the annotations can be used as input to determining concept-level annotations.

the method has the potential to generalize any existing database of instance-level annotations to concept-level annotations.


Rule 10, for example, models, as stated in the list that follows, how verbs of the type "a cânta" (to sing) conjugate in the indicative present, by having the first regular expression model the first person singular form "(eu) cânt" (in regular expression format:ˆ(.+)$), the second, model the second person singular form "(tu) cânţi" (ˆ(.+)ţi$), the third, model the third person singular form "(ei) cântȃ" (ˆ(.+)ȃ$), and so forth.

rule 10 catches the alternation t→ţ for the 2nd person singular, while modelling a particular type of verb class with a particular set of suffixes.


A real-word spelling error is hard to detect, as the erroneous word is not misspelled and fits syntactically into the sentence.

measures of contextual fitness are required to detect words that do not fit their contexts.


3 The API outputs plain text converted from Wiki-Markup, but the text still contains a small portion of leftover markup and other artifacts.

we perform additional cleaning steps removing (i) tokens with more than 30 characters (often URLs), (ii) sentences with less than 5 or more than 200 tokens, and (iii) sentences containing a high fraction of special characters like ':' usually indicating Wikipedia-specific artifacts like lists of language links.


However, the change does not need to characterize a real-word spelling error, but could also be a normal spelling error or a semantically motivated change.

we apply a set of semantic filters: Vocabulary The replaced token needs to occur in the vocabulary.


We found that even quite comprehensive word lists discarded too many valid errors as Wikipedia contains articles from a very wide range of domains.

we use a frequency filter based on the Google Web1T n-gram counts (Brants and Franz, 2006).


Previous work on evaluating real-word spelling correction (Hirst and Budanitsky, 2005;Wilcox-OHearn et al., 2008;Islam and Inkpen, 2009) used a dataset sampled from the Wall Street Journal corpus which is not freely available.

we created a comparable English dataset of 1,000 artificial errors based on the easily available Brown corpus (Francis W. Nelson and Kuçera, 1964).


As we have seen above, using smaller models will decrease recall to a point where hardly any error will be detected anymore.

we will now have a look on knowledge-based approaches which are less demanding in terms of the required resources.


We specified Subcat-LMF by a DTD yielding an XML serialization of ISO-LMF.

existing lexicons can be standardized, i.e.


(preposition) vs. Das hängt davon ab, wer es getan hat.

uniform Data Categories for English and German: the main challenge in developing Subcat-LMF has been the specification of DCs (attributes and attribute values) in such a way, that a uniform specification of SCFs in the two languages English and German can be achieved.


For the majority of SCFs, this mapping is defined on argument level.

lexical data is extracted from the source lexicons by using the native APIs (VN, GN)   the original VN-sense, a pairing of verb lemma and class, can be recovered by grouping lMF senses that share the same verb class.


ILS specifies similar semantic role information as GN; these few cases were mapped in the same way as for GN.

the LMF version of ILS, too, specifies less SCFs, but additional semantic predicates not present in the original.


They perform a conversion of these lexicons into a uniform, but non-standard representation format, resulting in a lexicon which is integrated at the level of verb senses, SCFs and lexical-semantics.

the result of their work is not applicable to cross-lingual settings.


Training data for this combination of language (Dutch), text type (FAQ) and domain (medical/neurological) is sparse.

we search for the type of training data that gives the best prediction results for this corpus.


This query expansion strategy, however, has the following drawbacks: (1) The strategy captures only inconsistencies that succeed the given company name in the original query.

near-duplicates which contain spelling errors in the company name itself are not found.


This makes it hard for the community to exploit their results on a large scale.

it diminishes the impact that these projects might achieve upon NLP beyond their original specific purpose, if their results were represented in a unified resource and could easily be accessed by the community through a single public API.


We addressed this issue by adding a sense attribute to the RelatedForm class.

in extension of LMF, UBY-LMF allows sense relations to refer to a form relation target and morphological relations to refer to a sense relation target.


These conversion routines extract lexical data from the source LSRs by calling their native APIs (rather than processing the underlying XML data).

all lexical information which can be accessed via the APIs is converted into UBY-LMF.


We can expect that these links have high quality, as they were entered manually by users and are subject to community control.

we straightforwardly imported them into UBY.


We can, however, show that UBY contains over 3.08 million unique lemma-POS combinations for English and over 860,000 for German, over 3.94 million in total, see Table 4.

we assessed the coverage on lemma level.


The LDA generative process is: (1) draw a latent topic z from a document-specific topic distribution P (t = z|d) then; (2) draw a word w from the chosen topic P (w|t = z).

the probability of producing a single copy of word w given a document d is given by: In standard LDA, the user needs to specify the number of topics T .


The final error is the mean of the per-example errors.

even though there is no separate test set, the prediction for each input is generated based on a model trained on examples which do not include it.


Notably, there are no clear "topical" divisions between T and V, like for example in text categorization: almost all words are very weakly correlated with either class, and semantically similar words can co-occur with different classes.

distributionally determined semantic classes are not helpful for the distinction.


We sum together the similarities of the character frequency and emotion curves to measure overall temporal similarity between the characters.

our first-order character kernel c 1 is: We use c 1 and equation 1 to construct a firstorder plot kernel (which we call k 1 ), and also as an ingredient in a second-order character kernel c 2 which takes into account the curve of pairwise frequencies u, u between two characters u and u in the same novel.


In Hellberg (1978), noun paradigms only permit the concatenation of suffixes to a stem.

the paradigms are identified with suffix sets.


A lexicon L is a finite set of inflection tables, As the n is fixed, this is a lexicon specialized to one part of speech.

a word is an element of the lexicon, that is, an inflection table of size n. an application of a smart paradigm P m to a word w ∈ L is an inflection table resulting from applying P m to the appropriate subset σ m (w) of the inflection table w, we assume that all arguments are existing word forms (rather than e.g.


Notice that code complexity is defined in a way that makes it into a straightforward generalization of the cost of a word as expressed in terms of paradigm applications in GF source code.

the source code complexity of a paradigm application is size(P m [w]) = 1 + m the complexity for a word w is its cost plus one; the addition of one comes from the application node for the function P m and corresponds to knowing the part of speech of the word.


The same is also true for G+N+R, which outperforms GNR and GN+R.

we only present the results for the single classifiers G, N, R and their combination G+N+R.


The advantage of this approach is that primitive predicates and their compositions are formally defined.

we can give a strict definition of semantic roles by grounding them to lexical semantic structures of verbs.


However, generally, a verb focuses on one of those events and this makes a semantic variation among verbs such as buy, sell, and pay as well as difference of syntactic behavior of the arguments.

focused event should be distinguished from the others as lexical information.


We can calculate the information from the LCS structure instead of coding it into role labels.

our extended LCS framework maintains generality of roles and the number of roles is smaller than other frameworks.


Recall that P (y, X y = 0) = 0 because of the assumption that a DEO appears in the NPI context that it generates.

p (y, X y = 1) = p (y, X y = 1) + p (y, X y = 0) = p (y) One iteration of EM to calculate this probability is equivalent to the distillation method of DLD09.


In terms of classification certainty, EM/distillation converges to a lower classification certainty score compared to our heuristic method.

our method better captures the assumption of one DEO per NPI context.


While the reordering of German implies movement of the German verbs into a single position, when reordering English, we need to split the English verbal complexes and, where required, move their parts into different positions.

we need to identify exactly which parts of a verbal complex must be moved and their possible positions in a German sentence.


movement of bought and read in figure 1 1 ).

in contrast to Genzel (2010), our approach does not have any constraints with respect to the position of nodes marking a verb within the tree.


In this work, we consider two possible positions of the negation in German: 1 It should, however, be noted that in German, the negative particle nicht can have several positions in a sentence depending on the context (verb arguments, emphasis).

more analysis is ideally needed (e.g., discourse, etc.).


Our main hypothesis was that it is possible to exploit these intuitions from Centering Theory and the idea of lexical chains for word order prediction.

we expected that it would be easier to predict the position of a referent in a sentence if we have not only given its realisation in the current utterance but also its prominence in the previous discourse.


However, the advantage of chains, especially chains of reiteration, is that they can be easily detected in any corpus text and that they might capture "topics" of sentences beyond the identity of referents.

we started out from the idea of lexical chains and added corresponding features in a statistical ranking model for surface realisation of German (Section 4).


Basically, our sentence-external feature model is built on the intuition that lexical chains or overlaps approximate discourse status in a way which is similar to sentence-internal morphosyntactic properties.

we would expect that overlaps indicate givenness, salience or prominence and that asymmetries between overlapping and nonoverlapping entities are helpful in the ranking.


We will assume that all constituents under a Vorfeld and Mittelfeld node can be freely reordered.

we do not check whether the word order variants we look at are actually grammatical assuming that most of them are.


We composed an annotation schema that reflects the coordination efforts for article improvement.

we manually analyzed a set of thirty Talk pages from the Simple English Wikipedia to identify the types of article deficiencies that are discussed and the way article improvement is coordinated.


Since a single turn may consist of several utterances, it can consequently comprise multiple dialog acts.

we designed the annotation study as a multi-label classification task, i.e.


The large number of CL labels compared to other labels from the same category is due to the fact that the Simple English Wikipedia requires authors to write articles in a way that they are understandable for non-native speakers of English.

the use of adequate language is one of the major concerns of the Simple English Wikipedia community.


Furthermore, which features carry this indexicality are specific to a context.

separating the socially meaningful variation from variation in linguistic features occurring for other reasons is akin to searching for the proverbial needle in a haystack.


Hypothesis: Our hypothesis is that models that explicitly represent the notion that accommodation occurs over a span of time with consistency of momentum will achieve better success at distinguishing between Real Pairs and Constructed Pairs than models that do not.

experimental Manipulation: using the model we have just described, we are able to test our hypothesis using a 2 × 3 factorial design in which one factor is the inclusion of direct links from the style of one speaker to the style of the other speaker, which we refer to as the DirectInfluence (DI) factor, with values True (T) and False (F), and the second factor is the inclusion of links from style states to and from Accommodation states, which we refer to as the IndirectInfluence (II) factor, with values False (F), Asymmetric (A), and Symmetric (S).


Turns were selected from the other Real pairs.

the Constructed Partner had similar content to the corresponding real partner on a turn by turn basis, but the style of expression could not be influenced by the Real Student.


Thus, the Constructed Partner had similar content to the corresponding real partner on a turn by turn basis, but the style of expression could not be influenced by the Real Student.

ideally we should not see evidence of speech style accommodation within the Constructed Pairs.


However, in our evaluation, we only tested our approach on conversations between male undergraduate students discussing the fall of the Ottoman Empire.

while our evaluation provides evidence that we have taken a first important step towards our ultimate goal, we cannot yet claim that we have a model that performs equally effectively across contexts.


In Example 5a, by the time the hearer processes the second occurrence of the door, she has already had a mental entity corresponding to the door (after processing the first occurrence).

the second occurrence of the door refers to an old entity.


These states basically process (part of) the tree l and project (with state q) to the subtree at position v. It is guaranteed that each such subtree (indicated by v) is reached only once.

the copying is "resolved" once the states of the form l, q, v are left.


The first (top row, left-most) rule of Figure 12 is non-linear in the variable y 2 .

we expand the calls q, ρ (y 2 ) and q , ρ (y 2 ).


In our experiments, however, annotators were allowed to assign just one tag to each tagged instance.

each annotator is described as a function that assigns a single member set to each instance A i (s) = {t}, where s ∈ S, t ∈ T .


The j-th row of CPM contains probabilities of assigning t i given that another annotator has chosen t j for the same instance.

the j-th row of CPM describes expected tagging confusion related to the tag t j .


We can then use the candidate with the highest probability as the translation for "request".

both models can predict translations for the English (minority language) in this example.


each τ is required to be a connected sub-graph of some synchronous-tree h. τ is composed of a subset of the rules from any h that generates x on the source side, such that there is a connected path between any two rules in τ .


In UMT all the needed information about the available context is stored in the carry κ i .

the computation of contextual features whose carry's size is bounded (like the LM) requires constant time.


For HMT b30, rescoring takes two thirds of the total decoding time.

rescoring is the most time consuming step in the pipeline.


This resulted in an accuracy decrease of 2.3 BLEU points.

these features are important for the success of the greedy approach.


In general, we believe that greedy-discriminative solutions are promising for tasks like MT, where there is not a single correct solution: normally there are many correct ways to translate the same sentence, and for each correct translation there are many different derivation-trees generating that translation, and each correct derivation tree can be built greedily following different inference orders.

the set of correct decoding paths is a reasonable portion of UMT's search space, giving a well-designed greedy algorithm a chance to find a good translation even without beam search.


Each requires different types of information to be resolved.

the task involves determining anaphoricity, resolving pronouns, and identifying semantic compatibility among mentions.


Assuming the identity ordering as canonical ordering in the Generalized Mallows Model, event types tending to occur earlier in the script should be assigned lower cluster IDs than event types occurring later.

whenever z e 1 < z e 2 , our the model predicts that two event descriptions occur in their natural order.


PageRank has also been viewed as the result of a Random Walk process, where the final rank of a given node represents the probability of a random walk over the graph which ends on that same node.

if we take the created Word-Net graph G with N vertices v 1 , .


For instance, there will be relatively more tweets on the East Coast in the early morning (when much of the West Coast is still asleep).

seeTweet builds the baseline distribution by querying the baseline term I, and using the first 50 tweets preceding each target tweet.


In addition, mean SeeTweet estimates of needs doing usage were lower for regions where respondents use needs done than for regions where they do not: −.93 versus −.49.

12 SeeTweet provides evidence that needs done and needs doing are in a geographically distinct distribution, while needs done and needs to be done are at most weakly distinct.


Users that remained ambiguous, were clearly fake or joke accounts, or who claimed multiple gangs were grouped together in an "Other" category, which accounts for 6.2% of the total.

94% of the users were classified into the 12 specific gangs mentioned above.


This is problematic because word-based features such as unigrams and bigrams, which are very frequently used, are highly likely to pick up on differences in topic (Schler, 2006) and possibly perspective.

in cases where linguistic style variation is specifically of interest, these features do not offer good generalizability (Gianfortoni et al., 2011).


These semi-supervised methods ultimately rely on a strong unsupervised model of PoS as their base.

further improvements in unsupervised models, especially in modelling tag constrains, should lead to improvements in semi-supervised part-ofspeech induction.


In many cases the words are merged into broader ambiguity classes because the Lex-HMM+LM uses the language model to model the morphology of words over individual partsof-speech, rather than entire ambiguity classes.

a word-type is likely to be assigned a given ambiguity class as long as at least one part-of-speech in that ambiguity class is associated with morphologically similar words.


Additionally, for seen but low-frequency words, we make use of the existing entry in the lexicon.

in a second step, we interpolate the count c corpus (w, cat) of a word and category with the supervised count of the same c tb (w, cat) (if it exists) to give the final smoothed count of a word and category c smooth (w, cat) (Eq.


In all experiments, the labeled data was scaled to match the size of the unlabeled data.

the scaling factor of labeled data was 10 for unlabeled data of 10M words, 20 for 20M words, etc.


Since these verbs are actually mid-frequency verbs in the supervised data, they have a correspondingly large occurrence frequency in the unlabeled data, occurring much more often than true unseen verbs.

the unlabeled data size is effectively magnified-as far as these verbs are concerned, the unlabeled data is approximately 11 times larger than it actually is.


That is the generator is not free to produce any NL variant verbalising the query but should produce a verbalisation that is linearly as close as possible, modulo the revision applied by the user, to the query before revisions.

for instance, given the DL query (2) and assuming a linearisation of that formula that matches the linear order it is presented in (see Section 4.2.1 below for a definition of the linearisation of DL formulae), sentence (2b) will be preferred over (2c).


As the trees are combined, the semantics is the union of their semantics modulo unification.

given the grammar and the derivation shown, the semantics of John often runs is as shown namely, named(j john), run(a,j), often(a).


Chart-Based Surface Realisation Given an FB-LTAG G of the type described above, sentences can be generated from semantic formulae by (i) selecting all trees in G whose semantics subsumes part of the input formula and (ii) combining these trees using the FB-LTAG combining operations namely substitution and adjunction.

for instance, in Figure 2, given the semantics l1:named(j john), lv:run(a,j), lv:often(a), the three trees shown are selected.


To ensure the appropriate syntax/semantic interface, we make explicit the arguments of a relation using the variables associated with the nodes of the query tree.

for instance, given the rightmost query tree shown in Figure  1, the flat semantics input to surface realisation is For each ontology, a lexicon mapping concepts and relations to FB-LTAG trees is automatically derived from the ontology using (Trevisan, 2010)'s approach.


Following the so-called consensus model (Power and Third, 2010), the current, template based version of Quelo generates one clause per relation 7 .

for instance, template-based Quelo will generate (5a) while our grammar based approach supports the generation of arguably more fluent sentences such as (5b).


The possible values of ρ range between 1 (where all systems are ranked in the same order) and −1 (where the systems are ranked in the reverse order).

an automatic evaluation metric with a higher absolute value for ρ is making predictions that are more similar to the human judgments than an automatic evaluation metric with a lower absolute ρ.


TM similarity metrics are used for both evaluating the expected helpfulness of previous translations for new workload translations and the metrics also directly determine what translations get provided to the translator during translation of new materials.

the algorithms that compute the TM similarity metrics are not only important, but they are doubly important.


Many translators find it counter-productive to use TM when the best-matching translation from the TM is not similar to the workload material to be translated.

many commercial TM products offer translators the opportunity to set a fuzzy match score threshold so that only translations with scores above the threshold will ever be returned.


Examples from our experiments reveal that this can lead to poor retrievals.

future work is called for to examine the extent to which the target language sides of the translations in the TM bank influence TM system performance and to investigate ways to incorporate target language side information to improve TM system performance.


For the design of this representation, we remove all lexical nodes (struck out nodes in Figure 2) and trees that do not span any of the target entities (not shown in the figure).

this structure is the smallest semantic structure that contains the two target entities.


in contrast to SST, PT kernels compare many more substructures.


Our approach to extract and classify social events builds on our previous work , which in turn builds on work from the relation extraction community (Nguyen et al., 2009).

the task of relation extraction is most closely related to the tasks addressed in this paper.


The set of context words C consisted of the 10,000 most frequent words occurring in this dataset, with the exception of stopwords from a standard stopword list.

a frequency vector for a tar- where c j C (|C| 10, 000), W is a set of target words in a particular evaluation dataset, and f w i c j is the co-occurrence frequency between the target word, w i and context word, c j .


We train a different SWSD classifier for each target word as in (Akkaya et al., 2009).

we need a different training dataset for each target word.


The induced clusters represent different usage patterns of a word.

we build more than two clusters, even though SWSD is a binary task.


There is not a single winner among all words.

perhaps choosing one single representation for all the words is not optimal.


In this example, silh const scores 3 1 2 Figure 1: Behaviour of selection function members 2 and 3 lower than 1.

member 1 will not be selected, which is the right decision in this example.


In this example silh const scores member 3 lower, which is farther away from already labeled members in the cluster.

member 3 will be selected to be labeled.


But there can be no certainty concerning the other reviews in the collection: all we have is a number of cues, also developed in collaboration with Duns, suggesting that a review may be genuine or deceptive.

this corpus is an example of a collection where it is not possible to acquire the actual label for all instances, and where clues of deception were treated as annotators who assign them heuristic labels.


This is because it is very difficult to find definite proof that an Amazon review is either deceptive or genuine.

most researchers recreate deceptive behavior in the lab, as done by Newman et al.


Lastly, two more features were added to the feature set, that is the length of the review, considered with and without punctuation.

in each fold of the experiment, the vectors of the reviews were constituted by 112 values: 2 corresponding to the length of the review, and 110 representing the (not normalized) frequency, into the review itself, of the selected n-grams of lemmas and POS.


Due to the free word order, simple phrase structure like that used for analysis of English is not enough to specify the syntax of German sentences.

we use the annotation scheme in the manually constructed German treebanks NEGRA and TIGER (Skut et al., 1997;Brants et al., 2002), which decorate parse trees with edge labels specifying the syntactic roles of constituents.


As the SCF inventory allows at most three complements in an SCF, such cases call for prioritising of verbal complements (e.g., subjects, objects, and clausal complements are preferred over PP complements).

the main verb empfehlen 'recommend' in (8), which has a subject, a dative object, a PP, and an infinitival clausal complement, is assigned the SCF ndi.


The edge labeller makes the simplifying assumption that verbal complements can be labelled independently.

it tends to annotate multiple complements as subject for each verb.


This has to do with the numerical dominance of subjects, which make up about 40% of all verb complements, more than three times the number of the next most common complement type (direct object).

we first collect all possible labels with associated probabilities that the edge labeller as-signs to each complement of a verb.


However, this is a costly and time-consuming task, as the participants will need a considerable amount of time to get used to the system.

we will evaluate Soothsayer by simulating somebody keying in a text, and counting how many keystrokes this virtual user does not have to do when using the system.


This distribution means that using all or a random selection of Twitter users is not likely to lead to good results, because for most users not much material is available.

only data from the 100 Twitter users for which the most material was harvested are used to build the idiolect models.


We use the alignment information between source and reference and therefore limit our analysis to pairs of aligned words, but nevertheless this shows that the adapted translation distributions model the test set distributions better than the baseline model.

the adapted distributions are not just more peaked but also more often peaked towards the correct translation.


Moreover, once the verb is identified, modeling verb errors is challenging because verbs fulfill many grammatical functions, resulting in a variety of mistakes.

the little earlier work done on verb errors assumed that the error type is known in advance.


The hard-decision type-driven approach defines a feature vector for a verb based on its type.

given the verb v and its type t, we define features φ(v, t, l) for each label l. the label is predicted as arg max l w T φ(v, t, l).


Passive Items The passive items are of the form: and state that a constituent with index l from category A was recognized from position j to position k in the sentence.


To eliminate interference introduced by other components, we use manually annotated gfbf information to build the graph.

the evaluations in this paper are able to demonstrate the promise of the overall framework itself.


From Table 2, we see that, regardless of the writer's sentiment toward the event, if the event is GOODFOR, then the writer's sentiment toward the agent and object are the same, while if the event is BADFOR, the writer's sentiment toward the agent and object are opposite.

the event type and the writer's sentiments toward the agents and objects give us constraints.


For any node without explicit sentiment, both the positive and negative values are 0.5, indicating a neutral node.

we build a sentiment classifier that takes a node as input and outputs a positive and a negative score.


If by discourse an object node is classified as positive, the positive value is 0.5 + random(0, 0.1) and the negative value is 1-positive value.

the positive value of a positive node is larger than its negative value, but not exceeding too much (similarly for negative nodes).


Recall that in Section 5.3, the definition of Ψ i,j is based on implicature rules and sentiment is propagated based on Ψ i,j .

this is also an evaluation of the performance of the graph model itself.


Intuitions: Users tend to formulate their Web search queries based on knowledge that they already possess at the time of the search (Paşca, 2007).

search queries play two roles simultaneously: in addition to requesting new information, they indirectly convey knowledge in the process.


Noncontiguous Attributes as Relations: Noncontiguous attributes extracted from fact-seeking queries are embodiments of relations linking the instances mentioned in the queries, on one hand, and the values being requested by the queries, on the other hand.

the method proposed in this paper can also be regarded as a method for the acquisition of relevant relations of various classes.


The instances are shown as the first instances in parentheses for each class in the earlier Table 2.

the class attributes are extracted using only the instances keanu reeves, boeing 737 and bugs bunny in the case of the classes Actors, Aircraft and Animated characters respectively.


But document sentences mentioning an instance do not necessarily refer to properties of the instance that people other than the author of the document are likely to inquire about.

even topranked extracted relations occasionally include less informative ones, such as comes into view for mount rainier, is on the table for madeira wine, or allows for features for javascript (Fader et al., 2011).


Comparatively, relations extracted via noncontiguous attributes from queries tend to refer to properties that have values that Web users inquire about in their search queries.

the relations extracted from queries are more likely to refer to salient properties, such as date/time (it) had its last eruption for mount rainier; length/duration (it) lasts for madeira wine; and manner (it) stores date information for javascript.


However, non popular entities, the majority in the social network, can also gain a great number of followers, by exploiting, for example, a follow-back strategy.

2 using solely the number of followers to quantify impact may lead to inaccurate outcomes (Cha et al., 2010).


Instead, in the present work we create a multilingual vector representation of a document d written in language L by concatenating the corresponding vector v L with the translated vector v L of d for language L .

we obtain a multilingual vector v LL , which contains lexicalizations in both languages.


Despite both using Wikipedia in some way, KBSim obtains much higher performance than CL-ESA thanks to the use of our multilingual knowledge graph representation of documents, which makes it possible to expand and semantically relate its original concepts.

in contrast to CL-ESA, KB-Sim can integrate conceptual and relational similarity functions which provide more accurate performance.


In practice, these existing methods are not flexible enough to handle parallel sentence pairs, especially those of SVO and SOV languages.

we permit further flexibility in SCFG aiming to effectively conduct long-distance reordering.


Its translation model is trained with the block-corpus, and two targetside language models (LMs) are trained with the block-corpus (referred to as block-LM) and the a pressure cylinder used high is hot water Figure 2: [N] node detection example original corpus (referred to as sentence-LM), respectively.

the sentence-LM can be trained using a larger-scale monolingual corpus.


For conducting abstraction based on syntactic structures, we merge a span governed by a dependency head as a node and represent it by a non-terminal in a parent node.

the i-th node N i consists of a sequence of lexical words w and non-terminals L that replace spans governed by heads in the corresponding child nodes: The edge E ij between a parent node N i and its child node N j corresponds to a governordependent relationship from the head in N i to its dependent w x in N j .


In order to achieve this goal, the principle of compositionality of linguistic structures, which states that complex linguistic structures can be formed through composition of simple elements, is applied to distributional vectors.

in recent years, the problem of composition within distributional models has caught many researchers' attention (Clark, 2013;Erk, 2012).


Genuinely bilingual people are rare, however.

judges are often people with demonstrated skills in the target language, and a working knowledge -often self-assessedof the source language.


Then, the projected vectors are: (1) and the correlation between the projected vectors can be written as: CCA maximizes ρ for the given set of vectors Σ and Ω and outputs two projection vectors v and w: Using these two projection vectors we can project the entire vocabulary of the two languages Σ and Ω using equation 1.

summarizing: the resulting vectors cannot be longer than the original vectors.


This is trivial to implement as the projection vectors V , W in equation 4 are already sorted in descending order of correlation.

in, Σ * k and Ω * k are now word vector projections along the top k correlated dimensions, where, V k and W k are the column truncated matrices.


the text span within which a mention establishes cohesive relations.

we propose to disambiguate mentions differently depending on their cohesive scopes (Section 2).


Conjunctions are often used for exemplifications.

mentions in conjunctions are often less prominent.


Straightforward PageRank calculations, when applied to the scientific literature, are hampered by two factors: on the one hand, the progression of time imposes a directional structure on the citation network.

pageRank values of older papers are systematically inflated as pageRank can only ever flow from newer to older papers (Walker et al., 2007).


Moreover, the more general a hypernym becomes -up to the level of WordNet root elements, such as entity -its predominant sense ceases to correspond to the sense intended in hyponymhypernym chains.

we never hear about going to visit an entity at the zoo.


However, these measures are less appropriate for testing whether a system can, in general, rank hypernyms over other relations.

we also report Mean Area Under the ROC Curve, or Wilcoxon-Mann-Whitney statistic (AUC), which gives equal weight to correct rankings at the top and bottom of the list, and also compensates for unbalanced data.


We show that this pattern also holds for visualworld referring expressions (REs), and moreover, that objects with sufficient visual prominence are treated as given.

we argue that the concept of salience used in surface realization should incorporate metrics from visual perception.


The standard information order of English clauses places given information first and new information later (Prince, 1981).

we observe that the non-right orders are used for larger objects, which is what we would expect if their visual perceptibility is sufficient to place them in common ground despite the lack of a previous mention.


The use of Euclidian distance is unfounded on coreference which handles a nominal annotation.

α will not be computed on this last corpus.


Likewise, reliability measures are higher on the opinion annotation, where we observe lower modifications of the reference.

we expect results like those presented on figure 4 to enable a direct interpretation of reliability measures.


The intention of our open Hearst-like patterns is to obtain a wide variety of text snippets which are not limited to simple analogy cases, so most snippets obtained will actually not be analogies at all.

additional filtering is required to find those which do actually contain an analogy between places.


In total, 336 workers participated in categorizing 87 snippets on average (some top contributors categorized up to 1,975 snippets).

895 snippets are classified as containing an analogy with a confidence of over 90% (confidence is computed as a weighted majority vote of worker judgments and worker reliability; with worker reliability resulting from workers failing or passing gold units in previous tasks).


This intuition was supported by a short evaluation in which we sampled 314 (10.3%) random snippets from this set and found none that had been misclassified.

the negative examples of our Gold set consist of the snippets manually re-classified by our expert annotators, and the snippets which had been classified with 100% confidence by the crowd-workers.


However, this can be a very small portion depending on the domain.

it may still be necessary to obtain new lexicons by manual annotation.


Finally, we removed the annotation of the two annotators whose answers were most different from the others': i.e., annotators with the biggest i all    values.

we got a Kappa score 0.432 on question Q1 and 0.415 on question Q2, which both mean moderate agreement.


We build SLT systems for five target languages: French, German, Russian, Hebrew, and Hindi.

our test systems are diverse typologically and trained on corpora of different sizes.


Due to segmentation differences in the released TED (text) corpora and then several automatic preprocessing stages, numbers of sentences for the same talks are not identical across languages.

we select English-French system as an oracle (this is the largest dataset), and first align it with the ASR output.


To formalise this idea into a generative model, we assume that the Giver act g depends only on the Follower act f .

we further assume that the semantic unit u depends on the set of waypoints w which it describes, and on the Giver's choice of dialog act g. u and f are conditionally independent given g. This provides a simple way of incorporating the different sources of information into a complete generative model 3 .


Case 2:l l, the type of content included in t x is really suitable for longer and detailed topic segments.

t x is likely conveying too much detail given its length i.e.


Case 3:l l, the content in t x is of the type that a skillful writer would include in a much shorter and less detail-oriented text.

t x is likely lacking appropriate details (laconic).


This effect could arise due to the fact these summaries are bags of unordered sentences.

verbose style could be perceived as hav-System 23's summary: Actual length = 253 words, Predicted length = 343 words, Verbosity degree = 90 A senior Scotland Yard police officer apologized to the parents of a black teenager slain five years ago in a race killing that has become the focus of debate over relations between police and ethnic minorities.


EXPANSION relations capture relations in which one argument provides a specification of the previous and relations in which one argument provides a generalization of the other.

these shift detection features could help distinguish EX-PANSION relations.


if Arg1 is causally related to Arg2, then the event described in Arg1 must temporally precede the one in Arg2.

cONTIN-GENcY and TEMPORAL can be highly confusable.


Quaker Oats Co. took over Stokely-Van Camp in 1983. we would need to write the following complex rules: is(Quaker Oats Co.,company), own(Stokely-Van Camp,Gatorade), took over (Quaker Oats Co.,Stokely-Van Camp), took over(Y, Z)→own(Z,Y), and carry out logic unification and resolution.

approaches that can automatically generate patterns (i.e., features) from syntactic and semantic representations of the Q/AP are needed.


Similar to UNSUP, this method is unsupervised but it considers the entire vocabulary of our text corpus rather than only food items.

this information can be considered as a generalization of all contextual words.


Determining whether a certain word/phrase is redundant is a stylistic question, which is difficult to quantify.

most annotation resources do not explicitly identify redundancies.


In this paper, we focus on the third part while assuming the first two are given.

our experimental task is: given a sentence known to contain a redundant phrase of a particular length, can that redundant phrase be accurately identified?


The summarizer based on KL-divergence assigns importance to sentences directly, in a complex function according to the word distribution in the sentence.

we use these summaries as potential indicators of word importance.